We propose to study gene regulatory noise in differentiated cardiomyocytes using single cell technology and use these functional data to identidy novel CVD risk loci. Genome-wide association studies (GWAS) have identified many variants associated with cardiovascular- related diseases, some of which are novel. However, similar to studies of other common diseases, these identified risk-associated variants fail to explain a significant portion of the genetic heritability of cardiovascular disease (CVD). Moreover, many associated variants are non-coding without obvious function, though putatively, these are involved in gene regulation. By combining results of GWAS with functional genomic data (for example, eQTL mapping), one can identify variants that influence molecular functions and are also associated with disease risk. Individual cells are expected to tolerate uncertainties in the form of both external and internal perturbations arising from variable environments or mutations. This is especially critical in the context of cell fate transitions during differentiation. It has long been recognized that robustness is an inherent property of all biological systems and is strongly favored by evolution. Depending on their different roles, the regulation of subsets of genes is required to be particularly robust in order to maintain the phenotype or the identity of a cell. Many dynamic physiological processes must also be robust, and as a result, loss or grain of robustness is associated with certain clinically relevant phenotypes and complex genetic disease. In particular, inter-individual variation in penetrance of a mutation associated with a disease, or the variability in response to drug, can also be explained at times by different degrees of robustness. Despite the importance of robustness and the regulation of noise as mechanisms that maintain high fitness, we still have a relatively poor understanding of how robustness is achieved and how is noise being regulated at the molecular level. To take first steps towards understanding how robustness is regulated in humans, and to identify loci where mutations can affect robustness and underlie CVD risk, we will use single cell technology to collect gene expression data from differentiated cardiomyocytes. We will use a detailed time course design and high- resolution single cell gene expression data. Our approach will allow us to map variance QTLs (robustness QTLs) in addition to the more standard expression QTLs. Robustness QTLs may be of particular importance as contributors to CVD risk, a diseases in which threshold effects are predominant. Specifically, we will propose to collect single cell RNA-seq throughout cardiomyocyte differentiation of 70 Hutterite individuals (Aim 1), map eQTLs, as well as genetic loci associated with inter-individual variation in gene expression robustness (Aim 2), and Integrate eQTL and robustness QTL mapping with GWAS results to identify variants associated with CVD risk and with CVD-related phenotypes (Aim 3).